Process control system using spatially dependent data for controlling a web-based process

ABSTRACT

System and method for controlling a process with spatially dependent conditions for producing a product with spatially dependent properties, e.g., a web/sheet-based process for producing a web/sheet-based product. Input data comprising a plurality of input data sets are provided to a neural network (analog or computer-based), each data set comprising values for one or more input parameters, each comprising a respective process condition or product property. The input data preserve spatial relationships of the input data. The neural network generates output data in accordance with the input data, the output data comprising a plurality of output data sets, each comprising values for one or more output parameters, each comprising a predicted process condition or product property. The output data preserve spatial relationships of the output data, which correspond to the spatial relationships of the input data. The output data are useable by a controller or operator to control the process.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of application Ser. No. 11/129,062,filed May 13, 2005, entitled “Neural Network Using Spatially DependentData For Controlling A Web-Based Process” in the name of L. PaulCollette, III et al.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to the measurement and control ofmanufacturing processes that produce web or sheet based products, andmore specifically, to use of a neural network with spatially dependentdata in the control of such processes.

2. Description of the Related Art

The quality of a manufactured product can often be more financiallycritical than the quantity that is produced. There are many standardsworldwide that provide guidelines for quality assurance betweensuppliers and customers. Maintaining standards of quality for a productmay require consideration of the specific properties of the product, aswell as the product's final use. The quality of a product is the resultof the physical integration of all the raw materials, equipment, andprocess and operator manipulations occurring during its manufacture.

Process control can be generalized as the collection of methods used toproduce the best possible product properties and process economiesduring the manufacturing process. Many manufacturing processes fall intoone of two categories based on the spatial or dimensional dependence ofproduct properties—longitudinal or bulk manufacturing; and web or sheetbased manufacturing. Longitudinal or bulk products can be considereddimensionally homogenous and can be measured or characterized with bulkproperties. Examples include plastic dowels, polymer threads, fluids,and so forth. Web-based products can be measured or characterized withspatially dependant properties. Examples include rolls and sheets ofplastic, paper, or other fibers, minerals and wood products, and evensome food products. Note that as used herein, the term “sheet” may referto both flat products and rolled products.

The challenges associated with web-based products require specialconsideration for the manufacturing process conditions and the productproperties due to the dimensional nature of web-based products. Impropercontrol of process conditions in web-based processes, in either thedirection of manufacture or across the direction of manufacture, canresult in products that are of little or no value to the final customer.In these situations the manufacturer will lose profit opportunity due tothe need to recycle and remanufacture the product, or sell the productat a lower price. Many customers purchase web-based products for use asa raw material in their own processes, which then convert the webproduct into final end user consumer products. Less than first qualityweb-based products are not typically accepted by customers. The abilityto effectively control web based processes and web-based productproperties plays a significant role in determining the profitability ofmanufacturing operations.

Quality and Process Conditions for General Process Control

FIG. 1 illustrates key concepts of a typical manufacturing process ingeneralized block diagram form. As FIG. 1 shows, raw materials 102 aretransformed by a process 104 under controlled process conditions intoproducts 106 with desired properties. FIG. 1 also presents exemplary rawmaterials, process conditions, and product properties for typicalmanufacturing processes. For example, raw materials 102 may include suchbulk feed materials as chemicals, fibers, minerals, energy, and parts orcomponents, among others. Process conditions may include such operatingparameters as flow, pressure, temperature, humidity, as well as speed,rate, and feed properties, among others. Example product propertiesrelated to quality may include weight, color, strength, composition,texture, and so forth.

Controlling Process Conditions

FIG. 2 shows a more detailed representation of the manufacturing processas it relates to the production of products with specific desiredproperties. More specifically, FIG. 2 provides a simplified overview ofvarious aspects of a manufacturing process, where the effectiveoperation of the process requires that the process conditions bemaintained at one or more condition set points so that the productproduced will have the product properties matching the desired productproperty targets.

As shown in FIG. 2, various raw materials 208 may be provided to aprocess 210 with various process conditions, including controllableprocess conditions, i.e., controller/actuator parameters, where theprocess produces a product 212 with various product properties. Theprocess 210 may be controlled in accordance with process conditiontarget values 202, which may be initialized with initial processcondition targets, as shown, but which may be adjusted based on feedbackfrom measured process and property data. As may be seen, productproperty measurement(s) 206 may be analyzed with respect to productproperty target value(s) 204, and an adjustment of process conditiontargets determined and applied in to the process condition target values202. As also shown, measurement(s) of process conditions may be analyzedwith respect to the process condition target values 202, and adjustmentsof controllable process conditions made to the process accordingly.Thus, the various components of the system may operate in conjunctionvia feedback mechanisms to control the process to produce a product withdesired properties.

The automation of manufacturing process controls allows the productionof products from complex manufacturing processes that cannot becontrolled by manual operation. In addition to manufacturing products athigher rates that are more economically favorable, automatic processcontrols allow the products to achieve more desirable productproperties, more consistently. These three factors: more productionthroughput, more desirable product properties, and a more economicaloperation, form the basis of process control, which can be summarized asutilizing scientific methods to gain economic leverage over themanufacturing process.

The process control tasks shown in FIG. 2 can be generalized into fivesteps that apply to both manufacturing processes for products that areboth longitudinal or bulk and web-based. It should be noted that thegeneral nature of these descriptions is not intended to ignore oroversimplify the efforts necessary to control the process conditions andproduct properties of every manufacturing process.

1) Setting of the initial process condition set points

2) Producing process condition measurements of the process conditions

3) Adjusting the controllable process states in response to processcondition measurements

4) Producing product property measurements based on product propertiesof the manufactured product

5) Adjusting the process condition set points to in response to theproduct property measurements.

Steps 2 and 4 involve measurements of process conditions andmeasurements of product properties necessary for control and financialsuccess of the manufacturing operation.

Thus, as FIG. 2 indicates, the manufactured product is defined by one ofmore product properties, where each product property is quantified by aspecific measurement, and the manufacturing process is operated toproduce the targeted level of each product property as determined by itsspecific measurement. Each specific product property, contributes to theoverall value of the manufactured product. As also shown, the productproperty target values, as well as process condition measurements (andinitial process condition targets), determine process condition targetvalues, which in turn may be used to adjust controllable processconditions of the process. Thus, the interplay of measured and targetproduct properties, measured and target process conditions, andadjustments made thereto, gives rise to a feedback system whereby thequality of the final product may be tuned and maintained to desirableends.

FIG. 3 illustrates a system representative of most manufacturingprocesses, where the end use and desired properties of the productsproduced determine the specific nature of the process and controls usedto adjust the process. In other words, the physical nature of theproduct being produced can dictate process design, raw materialconfiguration and the controls required to achieve the final productproperties.

The example of plastic dowel extrusion shown in FIG. 3 is a simplifiedprior art longitudinal or bulk process presented here for illustrationpurposes, although the general concepts described apply to more complexmanufacturing processes, as well. As may be seen, raw materials 102(such as plastic pellets, colorants, stabilizers lubricants, etc) areprocessed in an extruding machine 302 (that implements a process 104)under controlled process conditions (such as temperature, pressure,flow, etc.) to produce a product 106, specifically, plastic dowels 304,as shown. Examples of the controlled process conditions could includemelt materials thoroughly, mix materials uniformly, heat extrusionmechanism to preset temperature, maintain pressure through outextrusion, cool to desired temperature, and so forth. The product(dowels 304) in this example would be produced to have specificallydesired properties, such as, for example, color of the dowel, weight perstandard length, stiffness, tensile strength, etc.

In the general case, the actual product properties of a product producedin a process are determined by the combination of all the processconditions of the process and the raw materials that are used in theprocess. Process conditions can include, but are not limited to, theproperties of the raw materials, the process speed, the mechanicalmanipulation of the process equipment, and the conditions withinindividual operations of the process, among many others. As mentionedabove, the extrusion of a plastic dowel may be referred to as alongitudinal or bulk manufacturing process due to the relativeinsignificance of any latitudinal process or product considerations,i.e., due to the homogenous nature of the product in any direction otherthen the direction of manufacturing. Further examples of longitudinal orbulk products include liquids such as chemicals or petroleum products,solid particles of various sizes from polymeric raw materials to cement,or any other product where the properties have little or no crossmanufacturing direction variability, and that can be consideredhomogeneous when measured over small increments of manufacturing time.The desired properties of the plastic dowel can be based on time or therelative product position in the manufacturing process.

Quality and Process Conditions for Web-based Process Control

For the case of a process specifically designed to produce a web orsheet based product there are both longitudinal and latitudinalconsiderations related to the raw materials, the manufacturing process,and the product properties. Web-based product properties are similarlydetermined by the combination of all the process conditions of theprocess and the raw materials that are used in the process. Web-basedproducts can require that dimensional (i.e., 2 dimensional)considerations be given to the raw materials as part of the processbeing controlled. The previous example of a manufacturing process toproduce plastic dowels can be compared to a corresponding manufacturingprocess for the production of a continuous plastic sheet or web 402, asillustrated in FIG. 4.

A simplistic generalization can be made that the manufacturing processesfor the production of a plastic dowel and for the production of aplastic sheet involve approximately similar process component functionsaffecting the raw materials with corresponding manipulations oftemperature, pressure, flow, etc., over time. The resulting products(e.g., dowels 304 and sheets 402) differ with respect to their desiredproduct properties and how the process conditions are controlled toachieve the desired properties. Note that the plastic sheetmanufacturing process and its product properties differ from the plasticrod manufacturing process and its product properties due to the (two-)dimensional nature of the processes and properties. Like the extrudedplastic rod, the desired properties of the plastic sheet can be measuredbased on its position in the manufacturing process and can be referencedby time; however, the web-based plastic sheet must also havemeasurements of its manufacturing process and its product properties inthe latitudinal directions.

For typical web manufacturing processes producing web-based products,the latitudinal dimension for a process condition or a product propertyis referenced perpendicular to the direction of manufacturing. Thisposition reference perpendicular to or across the manufacturingdirection is typically referred to as the cross direction position or CDposition, while the product property position referenced to themanufacturing direction is typically referred to as the manufacturing ormachine direction position or MD position, each of which is illustratedin FIG. 5. Specifically, a 1D process/product (bottom) is shown to haveonly an MD direction 504, while the web-based product 402 is shown tohave an MD direction 504, as well as a CD direction 502, which may beseen to be perpendicular to the MD direction 504, i.e., to the directionof motion or travel.

Measuring Process Conditions and Product Properties.

As described above, there are specific steps in a generalized processcontrol strategy that require measurements of process conditions andmeasurement of product properties, however, there are manufacturingprocess measurements and product property measurements that can bedifficult to obtain due to the inherent nature of the physicalmeasurement, the location at which the desired measurement must betaken, or the time needed to procure an accurate measurement. In otherwords, certain process condition measurements can be difficult toreliably acquire due to location, environment, accuracy or otherconsiderations that limit the usefulness of the process condition datain a process control system or strategy, and various product propertymeasurements data can be difficult to acquire do to similarconsiderations. Product property measurements have an additionalconstraint on their usefulness associated with the time required toproduce an accurate and reliable measure of the specific productproperty. It is not uncommon for property measurements of certainproducts to require hours, even days or weeks before an accurate productproperty measurement is available, e.g., product properties involvingphysical performance or destructive testing such as strengths, shelflife, wear, color fastness, etc.

The economic viability of a manufacturing operation can be criticallydependant on the timely availability of accurate process conditionmeasurements and product property measurements. The inability to obtainaccurate and timely measurements can affect the efficiency of themanufacturing process as well as the quality of the products produced.

Web-Based Measurements

It can thus be appreciated that the dimensional nature of web-basedprocess conditions and web-based product properties that have theadditional requirement of cross manufacturing direction measurementsassociated with any point in time, requires unique consideration.

FIG. 6 illustrates a typical web-based product 402 and the relativedimensions related to the raw materials and the web-based manufacturingprocess, as well as a comparison of product property measurementconsiderations that can arise from the need to measure the same productproperty on two products made from roughly similar raw materials, butproduced through different manufacturing processes, specificallylongitudinal or bulk (e.g., dowel production), and web-based (sheetproduction).

Referring to the previous examples of the extruded plastic dowel and theextruded plastic film, a comparison of the two indicates that theweb-based product may require additional measurements of the samedesired property across the web-based product at a specific instant intime to characterize the desired product property, as compared to thecharacterization of the longitudinal or bulk product. In other words, asmay be seen in FIG. 6, the dimensional nature of a web-based productgenerally requires more measurements to provide a similar level ofprocess condition and product property measurement or characterization.Referring to FIG. 6, a measured product property (PP), for both thedowel product 304 and the web product 402 taken at times T₁ and T₂ arerepresented by respective labels. For the longitudinal or bulk product,there is only one measurement, per time, e.g., PP T₁, which denotesProduct Property (PP) at time T₁. For the Web-based product, there aremultiple measurements per time, PP T₁CD₁, which denotes ProductProperties (PP) at time T₁ at position CD₁, PP T₁CD₂, and so forth, foreach CD position across the product web. As also shown, such crossdirection measurements are also made for these CD positions at time T₂.Improper or incomplete measurement of the desired property across theproduct web (i.e., at the various CD positions) can result in improperor incomplete adjustment of process conditions targets and a product oflesser quality and value. As is well known in the art of web-basedproduct manufacturing, the total width of the manufacturing process andthe product can be segmented spatially into smaller individualincrements of the cross manufacturing direction width to facilitatehigher resolution measurement and control of the process conditions andproduct properties. Each of these individual spatial segments of themanufacturing process produces a corresponding spatial segmentation inthe product, e.g. longitudinal bands or strips running along the webproduct (in the time or manufacturing direction) associated with ordefined by corresponding CD positions, e.g., the respective CD_(n)positions at any or all time or manufacturing direction points. Forexample, a strip of the product running along the near edge of theproduct may be referred to as CD zone 1, profile zone 1, data box 1, orsimply CD₁. Thus, all the CD₁ measurements that are made in or takennear the front edge may be considered as being spatially contained in CDzone 1 or as being from that particular CD zone. It should also be notedthat different measurements reported as being made at a particular CDposition, e.g., CD₁, may not be taken from exactly the same position,e.g., due to the spatial requirements of some sensors. In other words, aset of measurements may be made within some particular CD zone, e.g., ina cluster within that zone or strip, and may be considered to be at thatlocation. Similarly, as described below, actual measured data may beused to synthesize additional data (e.g., via interpolation,extrapolation, etc.) associated with particular CD positions, even ifthe actual measured data were not taken from those exact positions.

For many web-based products, there are important product properties thatrelate to the final end use and quality of the product and thus requireadditional, or subsequent, product property measurements in order to beacceptable. For example, the printability of paper may not be known (asit is being produced on a paper making machine), until it is shipped toa printer for testing. It is also common in the case of web-basedproducts that some important web-based product property (or properties)cannot be measured directly. For example, some important properties mayrelate to the rate of variance of a property, i.e., may be based onminute differences that are spatially adjacent. In other words, theproduct's value or quality, and thus, the product's acceptability, mayrelate to the magnitudes of spatially adjacent product properties asthey vary across the product. As an example, a thickness variance of 1millimeter in sheet glass that occurs over a meter may not benoticeable, while that same variance over a centimeter may produce anoticeable distortion in the glass's transmissivity, e.g., a visible“ripple”, and so may result in an unacceptable product.

Having these spatially dependent measurements across a web-based productwould improve the overall control of the process conditions and/orproduct properties. Moreover, it may be beneficial for these measurementdata to be spatially coherent, where, as used herein, the term“spatially coherent” refers to data that preserve their spatialrelationships, e.g., that have associated position data whereby suchspatial distribution may be preserved, or that are organized in such away that preserves the spatial relationships or relative distribution ofthe data, e.g., the spatial relationships of the measurements (actual orsynthesized). It should be noted that “spatially coherent” does not meanthat the positions of the data are necessarily regularly spaced, or inany particular arrangement, but only that the spatial distribution ofthe data or spatial relationships among the data (which could berandomly distributed) are preserved, although such regular spacing iscertainly not excluded.

Note that as used herein, the terms “array”, “array data”, “spatialarray data” and “spatially coherent data” may be used to refer to datasets whose elements include positional information for the datacontained therein, or to data arranged to preserve the positionalinformation, not to the particular type of data structure used to storethe data.

The same requirement for multiple measurements applies to web-basedmanufacturing process conditions. Referring to the previous examples ofthe extruded plastic dowel and the extruded plastic film, web-basedmanufacturing processes may require additional measurements of the samedesired process condition across the web-based process at each specificinstant in time to characterize the desired process measurement in amanner corresponding to the characterization of the longitudinalprocess, as illustrated in FIG. 7, where the process conditionmeasurements are represented in FIG. 6 by respective labels, PC T₁ CD₁,etc., that denote Product Conditions (PC) at time T₁ at position CD₁,and so forth, for each time and CD position. Improper or incompletemeasurement of the desired process conditions across the process canresult in improper or incomplete adjustment of process conditions andthus in a product of lesser quality and value.

There are many instances in web-based manufacturing processes wherecritical data from multiple measurements of process conditions orproduct properties occur at the same instant in time, or that arereported as having occurred at the same instant in time, e.g., storedwith a single time stamp or other order denotation. These multipleprocess condition measurements and multiple product propertymeasurements can be contained in a data array with an informationstructure that can establish the positions of the individualmeasurements within the array structure relative to the crossmanufacturing direction position (CD) of the process condition or theproduct property. As noted above, within a data array, the spatial orpositional relationships of the individual measurements can bemaintained structurally, e.g., implicitly, via the data structure thatcontains the data, or explicitly, i.e., via additional informationincluded or associated with the data.

It is also common in web based process industries that process conditionand product property measurements are taken with devices that requiresome time to acquire or process the measurements, such as, for example,traversing measurement sensors that move across a field making a seriesof measurements in succession, in which case, the entire measurementdata array may be reported as having occurred at the same instant intime. In other words, although the series of measurements occurred overa span of time, the resulting data array may be reported and/or storedas a measurement data array with a single order or time stamp. It shouldbe noted that instead of including a time or order stamp, the data fromsuccessive measurements may simply be maintained (e.g., stored) in sucha way as to preserve their order. In other words, the temporal orderingmay be implicit (e.g., organizational), rather than explicit (i.e.,including time or order stamps).

Thus, the data array can also contain information or be organized in away that establishes the measurement position of the data array relativeto the manufacturing direction (MD) or to time, which may be referred toas temporally coherent or ordered. In some web-based manufacturingprocesses these data arrays containing process condition measurements orproduct property measurements can be referred to as ‘profile arrays’ orsimply as ‘profiles’.

FIG. 8 illustrates an example of a product property measurement dataarray represented as cross manufacturing direction measurements (CDproduct property measurements) or a ‘product property profile’, where inthis particular case, the product property is the web or sheetthickness, and where the measurements are displayed (graphically) withrespect to reference CD positions 1-20. FIG. 8 also shows an example ofa process condition measurement data array represented as crossmanufacturing direction measurement (CD process condition measurements),in this case, a ‘process condition actuator profile’, where in thisparticular case, the process condition is the process gap or opening,and where the measurements are also displayed (graphically) with respectto reference CD positions 1-20. These types of profile data arrays areused in one form or another in most web-based process industriesincluding, for example, paper, non-wovens, textiles, wood products,etc., among others.

FIG. 9 illustrates a typical technology used to measure web-basedproduct properties in the cross direction, although it should be notedthat other technologies may also be used. Note that an array ofstationary measurement sensors 904 may be used to acquire CDmeasurements spanning the product's width at each specified instant oftime, while traversing measurement sensors 902 make measurementsserially, with a sensor 908 moving back and forth across the web 402 asthe web travels along the manufacturing path, resulting in a “zigzag”measurement pattern.

Depending on the source of the product property measurement, directlyfrom product property measurement device within the process or from aproduct property measurement device subsequent to the process, the dataarray can be order or time stamped or otherwise marked to indicate themeasurement array data's relative position in the web-basedmanufacturing process, its relative position within the web-basedproduct, or its occurrence in time.

The multiple measurement data of process condition or product propertiesarising from web-based products must be mathematically reduced to asingle ‘average’ or otherwise representative order or time stamp valueto accommodate the limitations of current neural network modelingtechnology and methods.

Neural Networks as Predictors of Process and Property Measurements

Current computer fundamental models, computer statistical models, andneural network models can address certain specific process conditionmeasurement and product property measurement deficiencies related tothese physical or time constraints in certain manufacturing processes.An exemplary current neural network based approach to processmeasurement and control is disclosed in U.S. Pat. No. 5,282,261 toSkeirik, which is incorporated by reference below.

Currently available neural network technology can provide predictedvalues of process condition measurement data and product propertymeasurement data that may not be readily measured. For example, theprior art technique referenced above requires that the input data bespecifically time stamped for training and that the predicted data bespecifically time stamped for further use in a controller or in acontrol strategy. Time stamped process condition data and productproperty data are considered discrete data points, in that eachindividual measurement data point is detached or independent from anyother and clearly identified by a time stamp that can establish itsposition relative to the manufacturing process.

FIG. 10 illustrates a simplified exemplary embodiment of a neuralnetwork. As FIG. 10 shows, input data are provided to an input layer,including a plurality of input elements or nodes, each of which may becoupled to a plurality of elements or nodes comprised in a hidden layer.Each of these hidden layer elements or nodes may in turn be coupled toeach of a plurality of output elements or nodes in an output layer ofthe neural network. These output nodes provide respective output data,which may be used to predict and/or control a process. As is well knownin the art of neural networks, adjustable weights associated with thevarious node couplings are modified and set in a training phase, andsubsequently determine the resulting behavior of the neural network.This existing neural network technology and method is most readilyapplied to longitudinal manufacturing processes, as exemplified in theextruded plastic dowel example described above.

Prior art neural network applications in process control (see, e.g.,U.S. Pat. No. 5,282,261) have evolved through various computer-basedmodeling strategies including, for example, first principles modeling,statistical and empirical modeling; and non-conventional neuralnetworks. The current limitations for utilizing model-based neuralnetwork applications in web-based process control require statisticalaveraging or manipulations of the available spatial array data thatsignificantly reduces the usefulness of those very process condition andproduct property spatial array measurements.

Averaging spatial array data to produce a single data point in order toaccommodate modeling limitations effectively defeats the basic purposeof using computer modeling and neural network modeling to producemeasurements that are difficult to obtain. This type of data handlingrenders the neural network technology relatively ineffective fortreating web-based measurement for process conditions and/or productproperties.

Currently there are considerable deficiencies in conventional approachesto obtaining desired measurements for web-based manufacturing processconditions and web-based product properties. Specifically, there are noavailable methods for directly utilizing spatial array based data thatare typically derived from web-based manufacturing processes, nor arethere well-defined web-based product properties for modeling andpredicting desired measurements of process conditions and productproperties. Web-based manufacturing processes and products thus presenta unique challenge to existing neural network technologies due to theneed to accommodate array based measurements that are referenced in boththe manufacturing direction and the cross manufacturing directions, aswell as in time.

SUMMARY OF THE INVENTION

Various embodiments of a computer-based neural network process controlsystem and method capable of utilizing spatial array based input data(i.e., spatially coherent data) for the prediction of spatial arraybased process conditions and/or product properties (i.e., spatiallycoherent predicted process conditions and/or product properties) aredescribed. In this approach, a trained neural network utilizingspatially coherent input data produces a spatially coherent array ofpredicted values of process conditions and/or product properties thatcannot be readily measured. The predicted values, including theirspatial relationships or distribution, may be stored in a data network,e.g., in a historical database, and supplied to a controller used tocontrol a web based process for producing a product, or presented to aprocess operator for use to control a process for producing a web orsheet based product.

The computer neural network process control system and method mayincorporate data in array forms, and optionally in discrete forms aswell, where the data may be from multiple databases and/or from otherdata sources that may include the manufacturing process operation, theproduct property testing operation, and/or product customers operation,among others.

In one embodiment, the method for controlling a process with spatiallydependent conditions for producing a product with spatially dependentproperties may include providing input data to a neural network, wherethe input data include a plurality of input data sets, each includingvalues for a set of one or more input parameters, where each inputparameter in the set includes or represents a respective processcondition or product property, and where the input data preserve spatialrelationships of the input data. The neural network may generate outputdata in accordance with the input data, where the output data include aplurality of output data sets, each including values for a set of one ormore output parameters, each output parameter including a predictedprocess condition or product property, where the output data preservespatial relationships of the output data, and where the spatialrelationships of the output data correspond to the spatial relationshipsof the input data. The output data are useable by a controller oroperator to control the process. In preferred embodiments, the outputdata are provided to the controller or operator, and the controller oroperator may then control the process in accordance with the outputdata.

It should be noted that the spatial coherence of the input data and/orthe output data may be maintained in various ways. For example, in someembodiments, the data may include position information, e.g., includedin, or associated with, each data set. In some embodiments, the data maybe stored in such a way that the spatial relationships among the dataare maintained implicitly, e.g., via the data structure itself.

In one embodiment, the neural network may be configured by a developerwho supplies neural network configuration information, e.g., the numberof layers in the network, the number of nodes in each layer, the rulesgoverning the neural network's mechanisms and evolution, and so forth.In one embodiment, the developer may easily configure the neural networkusing a template approach. For example, a graphical user interface maybe provided whereby the developer may interactively specify the neuralnetwork architecture, as well as other details of the network'soperation.

In some embodiments, the training of the neural network may accomplishedusing training input data having predetermined dimensional arrayconfigurations or formats for both space and time. Training may be basedon discrete time-based data as well as spatial array data with orwithout a time base. Training array data may be compared to predictedoutput array data values produced by the neural network.

In preferred embodiments, a modular approach may be utilized for theneural network. For example, certain specific neural network modules maybe provided based on the specific manufacturing process and desiredproduct properties.

In preferred embodiments, the neural network may be used to control aprocess with spatially dependent conditions for producing a product withspatially dependent properties, e.g., to control a web-based process forproducing a web-based product. The process may be operated, and processconditions and/or product properties measured at a plurality ofpositions to generate measurement data including measured processconditions and/or product properties, where the measurement datapreserve spatial relationships among the measurement data, i.e., themeasurement data are spatially coherent. Additional data may optionallybe synthesized based on the measurement data. The measurement data maybe provided to a neural network as an input data array, and the neuralnetwork may produce output data in response to the measurement data,where the output data include predicted process conditions and/orproduct properties, and where the output data have spatial relationshipsthat correspond to the spatial relationships of the measurement data.

Controller output data may be computed using the neural network outputdata as controller input data in place of measurement input data, and anactuator array controlled based on the controller output data to changea controllable process state using the actuators in accordance with thecontroller output data. This process of operating, providing, producing,computing, and controlling may be performed in an iterative manner toproduce the product with desired properties.

Alternatively, the output data from the neural network may be providedto a human operator, who may then manually control the process, e.g., bycontrolling an actuator array based on the output data to change acontrollable process state using the actuators in accordance with theoutput data. As with the automatic control embodiment, this process ofoperating, providing, producing, and manually controlling may beperformed in an iterative manner to produce the product with desiredproperties.

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the present invention can be obtained when thefollowing detailed description of the preferred embodiment is consideredin conjunction with the following drawings, in which:

FIG. 1 is a block diagram illustrating a typical manufacturing process,according to the prior art;

FIG. 2 is a more detailed block diagram illustrating a manufacturingprocess related to the production of products with specific desiredproperties, according to the prior art;

FIG. 3 illustrates a typical manufacturing process related tolongitudinal or bulk production, according to the prior art;

FIG. 4 illustrates a typical manufacturing process related tolongitudinal or bulk and latitudinal production compared to the processof FIG. 3, according to the prior art;

FIG. 5 illustrates manufacturing position and cross direction position,according to the prior art;

FIG. 6 illustrates a typical web-based product and relative dimensionsdue to the raw materials and web-based manufacturing process, comparedto those of a typical longitudinal manufacturing process, according tothe prior art;

FIG. 7 illustrates measurements of the same desired process property orcondition across the web-based process at a specific instance in time,according to the prior art;

FIG. 8 illustrates cross direction (CD) product property and processcondition profiles (measurement data arrays), according to the priorart;

FIG. 9 illustrates a typical technology for measuring web-based productproperties in the cross direction, according to the prior art;

FIG. 10 illustrates a simplified exemplary neural network architecture,according to the prior art;

FIGS. 11A and 11B are high-level block diagrams of embodiments of asystem for manufacturing process control using a neural network thatutilizes spatially dependent data;

FIG. 12 flowcharts one embodiment of a method for using a neural networkthat utilizes spatially dependent data;

FIG. 13 is a simplified block diagram representative of a neural networkthat receives and produces spatially dependent data; and

FIGS. 14A and 14B flowchart embodiments of a method for controlling amanufacturing process with a neural network that uses spatiallydependent data.

While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and will herein be described in detail. Itshould be understood, however, that the drawings and detaileddescription thereto are not intended to limit the invention to theparticular form disclosed, but on the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the present invention as defined by the appendedclaims.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Incorporation by Reference

The following references are hereby incorporated by reference in theirentirety as though fully and completely set forth herein:

U.S. Pat. No. 5,282,261, titled “Neural network process measurement andcontrol”, filed Aug. 3, 1990, issued Jan. 25, 1994, and whose inventorwas Richard D. Skeirik.

FIGS. 11A and 11B—Embodiments of a System for Process Control Using AnArray-Based Neural Network

FIGS. 11A and 11B are high-level block diagrams of embodiments of asystem for manufacturing process control using an array based neuralnetwork. More specifically, the systems of FIGS. 11A and 11B aredirected to process control of web-based (sheet-based) manufacturingusing a neural network that utilizes data related to or describingspatially dependent product properties and process conditions, i.e.,spatially coherent data describing product properties and processconditions. FIG. 11A illustrates a system that includes an automatedcontroller, whereas FIG. 11B illustrates a system where a human operatormanually controls the process.

As is well known in the art of process control, control of processes,e.g., manufacturing processes, generally involves a feedback loopbetween the process, and a controller or an operator performing manualcontrol, where measurements of various properties of the process andproduct are fed back to the controller, which may adjust aspects of theprocess accordingly to maintain or modify product qualities. Inpredictive process control, the controller may include or be coupled toa model of the process, and may use the model to make predictions orestimates regarding the process behavior or product qualities orproperties given certain process conditions. Based on these predictionsor estimates, the controller (or operator) may make adjustments to theprocess to affect a desired outcome, e.g., to produce products withdesired properties, or to manage the process in a desired manner. Anoverview of a typical manufacturing process control system is providedabove with reference to FIG. 2.

As FIG. 11A shows, a manufacturing process, specifically, a web-basedmanufacturing process 1116 that receives raw materials 1114 and producesa web or sheet product 1118, may be coupled to a process control system,e.g., comprising regulatory control 1110 and/or supervisory control1108, data network 1106, for example, live process data feeds,historical databases, etc., and a neural network 1104, including aneural network configuration module or process 1102, according to oneembodiment of the present invention. It should be noted that as usedherein, the term “data network” may refer to any source or collection ofsources for data related to the process or product, including, forexample, single or multiple, e.g., networked, mass storage devices, livesensor data, simulations, laboratories, human operators, and so forth.

As is well known in the art of automated process control, thesupervisory controller 1108 generally manages the regulatory controller1110, i.e., determines the overall strategy, while the regulatorycontroller 1110 actually implements the strategy specified by thesupervisory controller 1108, i.e., controls the process itself, e.g.,via actuators. Note that the system of FIG. 11B differs from that ofFIG. 11A only insofar as the supervisory controller 1108 is replacedwith manual operator control 1109, e.g., a human operator that manuallycontrols the process, where the manual operator control 1109 onlyreceives information from the historical database 1106 (i.e., does notprovide data to the database). Thus, the descriptions below addressed toall other aspects of the system of FIG. 11A also apply to the system ofFIG. 11B.

As shown in FIG. 11A, data (represented by arrows) flow between thevarious components of the system, forming feedback loops that mayoperate to keep the components updated regarding product properties andprocess conditions, and to direct and determine operation of theprocess. As shown, information from measurements, labeled “M”, i.e.,measurement data 1122, which may also be referred to as sensor data, maybe used to maintain currency of the data network 1106, e.g., historicaldatabase, as well as to provide process and/or product property feedbackto the regulatory control system. For example, as FIG. 11A indicates,measurement data regarding the state of the raw materials and theprocess may be stored in the historical database 1106. Similarly, datacharacterizing the product 1118 may also be provided to the historicaldatabase 1106. In the embodiment shown, the product data may be providedto a laboratory 1112, which may analyze or otherwise process the dataand provide the results to the data network 1106 (e.g., historicaldatabase). Note that in some embodiments, the product itself may beprovided to the laboratory 1112, which may analyze the product, e.g., tocharacterize the product with respect to desired properties, and providethe results to the data network 1106 (e.g., historical database).

Conversely, information directed to actuator control 1124, labeled “A”,may be provided by the regulatory controller 1110 to the process 1116,thereby driving or specifying actuator behavior for the process 1116.Note that the term “actuator” may refer not only to mechanicaleffectors, but also to any means used to control the manufacturingprocess, e.g., mechanical, electrical, hydraulic, optical, logical, etc.As FIG. 11A also shows, information may also be communicated between theneural network 1104 and the neural network configuration module 1102,e.g., to configure and/or train the neural network 1104, as well asbetween the neural network 1104 and the data network 1106, e.g., thehistorical database, e.g., for training the neural network, populatingthe historical database with predicted or estimated values, providinglive data from the process to the neural network, etc.

In a preferred embodiment, the system uses spatially coherent data,e.g., in the form of a spatial data array, from the suitably designedand configured neural network 1104 to replace process spatial datameasurements or laboratory spatial data measurements as input to acontroller (1108 of FIG. 11A) or control strategy (e.g., implemented byan operator 1109 of FIG. 11B), or as set points to a controller orcontrol strategy. One embodiment of such a neural network is describedin detail below. The neural network 1104 may use readily available andreliable process condition measurements and product propertymeasurements from the process 1116 or from subsequent product propertymeasurements as input data, and may produce predicted spatially coherentdata values of process conditions and/or product properties as outputdata. In some embodiments, the neural network may utilize synthesized orcomputed input data. For example, in cases where input data areincomplete, additional input data may be determined, e.g., based onthose data that are available, e.g., via interpolation, extrapolation,or more sophisticated processing or modeling. In some embodiments,models may be used to derive values of additional parameters based onthose parameters that can be measured. In some embodiments, the inputdata may be pre-processed prior to providing the data to the neuralnetwork, e.g., to remove and/or replace unusable or invalid data (e.g.,outliers) with valid data, put the data into a more usable form, orotherwise manipulate or pre-process the data. Further details regardingthese embodiments are provided below.

Thus, the neural network 1104 may receive input data in the form of aspatially coherent data set, e.g., an array of records that preservesthe spatial distribution of the input data, and may producecorresponding output data, e.g., in the form of a spatially coherent setof predicted process conditions and/or product property values. Theseoutput data may then be provided as input to a controller or controlstrategy, as set points to a controller of control strategy, or as theset points to a manually implemented operator control strategy, asmentioned above. Note that the use of ordered or order/time stamped(spatially coherent) array data as inputs to a neural network thatgenerates similarly spatially coherent output data rather than a neuralnetwork single point output facilitates or provides the spatiallycoherent predictive and control data needed to effectively controlweb-based manufacturing processes.

As indicated above, in some embodiments, the data network 1106, e.g.,historical database(s), may be used to provide spatially dependentprocess conditions and/or product properties, e.g., historical spatiallydistributed (and spatially dependent) process condition measurement dataand/or product property measurement data, to the neural network,although it should be noted that data from other sources may also beused as desired, including live feeds from the process, laboratory,etc., synthesized data, and so forth.

In some embodiments, when new data are added to the data network, e.g.,historical database(s), additional training of the neural network 1104may be performed to maintain currency of the neural network 1104, i.e.,to keep the operational behavior of the neural network 1104 up to date.The historical database(s) and processes used to manage the databases(s)may be referred to as “historians”. In one embodiment, new data providedto the historians may automatically initiate retraining, which may occuron-line and in real time, or off-line, as desired.

As mentioned above, in preferred embodiments, the output of the systemand methodology may be incorporated into automatic control systemstructures, e.g., supervisory or/or regulatory, or as part of a manuallyinitiated control procedure. In some embodiments, a modular architecturemay allow the system to build multiple neural networks from multipledatabases associated with a process. The system may provide controlfunctions such as supervisory, expert, and/or statistical and analyticalfunctionality, to support automatic and/or manual control. It should benoted that the controller or controllers used in various embodiments ofthe present invention may be any type of controller, i.e., may utilizeany type of technology suitable for controlling the process, includingneural networks, expert systems, fuzzy logic, support vector machines,and so forth, as desired.

It should also be noted that in some embodiments, other types ofnonlinear models may be used in addition to, or in place of, the neuralnetwork 1104, including, for example, support vector machines, expertsystems (e.g., rule-based systems), physical models, fuzzy logicsystems, partial least squares, statistical models, etc.

FIGS. 12 and 13—Method for Utilizing an Array-Based Neural Network

Turning now to FIG. 12, a high-level flowchart is presented describing amethod for operating a neural network for use in process control. Notethat in various embodiments, some of the method elements shown may beperformed concurrently, in a different order than shown, or may beomitted. Additional method elements may be performed as desired.

As FIG. 12 shows, in 1202, a input data may be provided as input to aneural network, where the input data include or describe processconditions and/or product properties for a process, e.g., a web basedmanufacturing process that operates to produce a web based product, andwhere the input data preserve spatial relationships of the input data,e.g., the input data are spatially coherent. As described above, webbased products may include, but are not limited to, paper, sheet(including flat and rolled product) plastic or polymer, particle-board,wood or organic composites, sheet metal (e.g., foil or thicker metalsheeting), sheet composites, sheet glass, sheet fiber-glass, laminates,textiles, and food products, such as candy and gum, among others.

The process conditions may include any attribute or aspect of theprocess related to the product manufacture. Example process conditionsmay include, but are not limited to, temperature, position (e.g., gap),pressure, humidity, voltage, and current, flow, speed, rate, feedproperties (e.g., of materials), and raw material properties, amongothers. Example product properties may include, but are not limited to,weight, moisture content, color, strength, stiffness, composition,flatness, texture, thickness, gloss, runnability, printability, andhardness, among others. For example, the above parameters or propertiesmay be important quality metrics for paper production, laminar products,and so forth.

As will be described below in more detail, in some embodiments, theinput data may be provided in the form of multiple data sets, where eachdata set may include respective values for a set of input parameters,where each input parameter in the set comprises a respective processcondition or product property. In preferred embodiments, each data setmay include, or may be associated with, information indicating thespatial distribution of the input data. In other embodiments, the inputdata sets may simply be organized or arranged to preserve this spatialdistribution, e.g., in a spatially coherent data structure.

Note that the input data may be provided from any of a variety ofsources. For example, the input data may be obtained by measuring and/orsynthesizing one or more process conditions at each of one or morespatial positions in a production line of the process, and by measuringand/or synthesizing one or more product properties at each of one ormore spatial positions on the product.

As mentioned above, measurements may be made using one or more sensorsoperable to detect physical attributes of the product and/or processconditions, such as, for example, traversing sensors and/or staticsensor arrays, although any other types of sensor or sensorconfiguration may be used as desired.

Data synthesis may also be performed using any of a variety of methods.For example, in the case that some measurements (of process conditionsand/or product properties) are available, but not at the spatialfrequency or resolution desired, interpolation and/or extrapolation(e.g., linear or nonlinear) may be used to generate the “missing” data.In other embodiments, models may be used to generate the additionaldata. For example, nonlinear models, such as trained neural networks orsupport vector machines which may operate to model data sets underspecified constraints, may receive the available measurement data asinput, and may generate the additional data as output.

In some embodiments, the additional data may include values of the sameparameters measured but at positions where such measurements were notmade, e.g., may be “interstitial” data (e.g., interpolated), or extendeddata (e.g., extrapolated), such as temperature values for regions notcovered by sensors. In some embodiments, the additional data may includevalues of parameters that may not be directly measurable, but that maybe derivable from measurements. For example, assume that temperature andpressure are measured process conditions, e.g., in an enclosed portionof the product line, but that moisture content is (for some reason) notmeasurable in this region. Assume that the composition of the product isknown. A first principles model based on the relationship betweentemperature, pressure, and moisture content, may then be used togenerate the moisture data.

In 1204, the neural network may generate output data, where the outputdata include or describe predicted or estimated product properties, andwhere the output data preserve spatial distribution of the output data,e.g., preserve spatial relationships among the data, e.g., via includedor associated position information, or via a spatially coherent datastructure. For example, in a paper manufacturing process, datadescribing process conditions such as temperature, position (e.g., gap),pressure, material feed rates and mixes, etc., may be provided to theneural network, which may then predict a resulting product property,such as for example, the paper's moisture content, thickness, etc. Inpreferred embodiments, the predicted product properties are useable tocontrol the manufacturing process.

One embodiment of a neural network suitable for implementing embodimentsof the present method is illustrated in FIG. 13, although it should benoted that the neural network architecture shown is meant to beexemplary only, and that other neural network architectures may be usedas desired. FIG. 13 indicates the dimensional or spatial requirementspertaining to web based manufacturing processes and web based productsas regards the need to consider the spatial relationships of processcondition measurements and/or product property measurements with respectto their position in the process (i.e., in time or with respect to themanufacturing direction or MD and with respect to the crossmanufacturing direction or CD) and with respect to the their position inthe product (i.e., in time or with respect to the manufacturingdirection or MD and with respect to the cross manufacturing direction orCD).

As FIG. 13 shows, in this embodiment, spatially coherent input data 1310are provided as input to a neural network 1320, which produces as outputspatially coherent output data 1330. In the embodiment shown, the inputand output data are in the form of data arrays. Note that the number ofelements shown in each array is meant to be exemplary only, and is notintended to limit the size of the arrays. Note also that while the term“array” is used herein, any type of data structure that preserves theorganization of the data may be used as desired, as mentioned above.

As indicated in FIG. 13, the spatially coherent input data preferablyincludes a plurality of data sets, each including values for a set ofinput parameters 1314, where, as described above, each input parameterdescribes, corresponds to, or includes, a respective process conditionor product property for a respective spatial position. For example, asone example relating to paper production, each data set may include aparameter value describing the process temperature at a respective pointrelative to the manufacturing process, e.g., relative to themanufacturing direction or MD as described above, and also associatedwith a particular position on the product relative to the crossmanufacturing direction, i.e., the CD, at a particular time. The dataset may also include one or more values for parameters describingproperties of the paper at that position, such as for example, thepaper's moisture content, thickness, and so forth. The input data arepreferably ordered or time stamped with a value indicating orrepresentative of the order or time at which the data originated, e.g.,the time of measurement, which in some cases, such as when traversingsensors are used, may be an average, median, initial, etc., time of themeasurement times (or MD positions) performed during the traversal.Alternatively, the input data may simply be stored and used in anordered manner such that explicit order or time stamping is notrequired.

As FIG. 13 shows, in this exemplary embodiment, the spatially coherentinput data include multiple (in this particular example embodiment, sixor more) input data sets, each including one or more product propertiesat a respective CD position, labeled “PP₁ CD_(n)”, and one or moreprocess conditions at the respective CD position, labeled “PC₁ CD_(n)”,where the subscripts “n”, “n+1”, etc., denote the distinctness of eachrespective CD position, and may also indicate the spatial order of thedata sets. Boxes with ellipses denote additional parameter values in thedata sets, e.g., additional process conditions and/or productproperties, although it should be noted that in various embodiments, thedata sets may each include one or more process condition values and/orone or more product property values, i.e., the number of parametersshown in each data set is for illustration purposes only, and is notintended to limit the input parameters to any particular number or type.The vertical dots at the top and bottom of the input data 1310, neuralnetwork 1320, and output data 1330, indicate that additional elements ofeach may be included, e.g., additional input data sets 1312, additionaloutput data sets, and so forth, as desired.

It should also be noted that in some embodiments, in addition to thespatially coherent input data, a single or average data value may alsobe provided as input to the neural network. For example, one or moresingle or average data values may be provided corresponding to orrepresenting one or more general process conditions or productproperties, e.g., properties or conditions that are not specific to anyparticular position but are representative of all positions as astatistical or otherwise representative average.

As noted above, the neural network 1320 may have any of variousarchitectures. For example, in some embodiments, the neural network 1320may be a single network, e.g., may have a monolithic architecture.Alternatively, as indicated in FIG. 13, in some embodiments, the neuralnetwork 1320 may include a plurality of sub-networks 1322, i.e.,sub-neural networks. For example, in the embodiment shown, the neuralnetwork 1320 includes a respective sub-network for each data setincluded in the input data. As also indicated in FIG. 13, eachsub-network 1322 may have input nodes corresponding respectively to theinput parameters of a data set. In some embodiments, a sub-network 1322for a specific set of input parameters 1314, e.g., product propertyPP_(j) for a specific time (e.g., PP_(j) T_(m)) at a specific location(e.g., PP_(j) T_(m)CD_(n)), may interact with adjacent or locallyadjacent sub-networks. In other words, in some embodiments, thesub-networks may be interconnected with adjacent sub-networks (or evenwith their neighbors, and so forth). Note that the sub-networks shownare only meant to be generally representative, and are not intended torepresent actual sub-network architectures. Following the paperproduction example above, each sub-network may have input nodes fortemperature, paper moisture content, and paper mass, and so forth. Thus,each sub-network may correspond to a spatial position for which processcondition and/or product property values are provided (via measurementor synthesis).

Each sub-network may operate to receive these parameter values, and asindicated, may generate respective one or more predicted productproperty and/or process condition values 1332. As FIG. 13 indicates, inthis exemplary embodiment, the spatially coherent output data includemultiple (in this particular case, six or more) output data sets, eachincluding one or more predicted product properties at a respective CDposition, labeled “P-PP_(A) CD_(n)”, and one or more predicted processconditions at the respective CD position, labeled “PC_(A) CD_(n)”,where, as noted above, the subscripts “n”, “n+1”, etc., denote thedistinctness of each respective CD position, and may also indicate thespatial order of the data sets.

For example, following the same paper production example, eachsub-network may provide a predicted value for the paper's moisturecontent corresponding to the input parameter values (temperature, mass,thickness, production rate, etc.), as well as (in this particularexample embodiment) a respective predicted process condition, such as,for example, humidity (e.g., due to evaporation from the product). Asmentioned above, the output data may be arranged or included in aspatially coherent output array 1330, and/or may be output with positioninformation preserving the spatial distribution of the data. Note thatin this particular embodiment, due to the correlation between the inputdata, the sub-networks, and the output data, the neural network 1320 mayitself be considered to be spatially coherent, e.g., in a logical sense.For example, each respective input data set, sub-network, and outputdata set, may correspond to a respective CD zone, profile zone, or databox, with respect to the product.

Thus, the neural network may generate spatially coherent output data inaccordance with the input data, where the output data comprisesspatially coherent values for an output parameter comprising a predictedprocess condition and/or product property. As noted above, in preferredembodiments, the output data are useable by a controller or operator tocontrol the process.

As shown in 1206, the predicted product properties, i.e., the spatiallycoherent output data, may optionally be provided to a controller oroperator for use in controlling the manufacturing process. For example,referring back to FIGS. 11A and 11B, the predicted product propertiesmay be used by or stored in the data network 1106, e.g., may be storedin the historical database, and then may be retrieved, e.g., by thesupervisory controller 1108, which may determine a strategy forachieving desired product properties, or by an operator 1109, which mayindicate this strategy to the regulatory controller 1110, which may inturn implement the strategy by controlling actuators in the process.

FIGS. 14A and 14B—Methods for Controlling a Process Using a NeuralNetwork

FIGS. 14A and 14B are high level flowcharts of methods for controlling aprocess, e.g., a web-based manufacturing process, using a neuralnetwork, according to various embodiments of the present invention. Morespecifically, the method of FIG. 14A relates to automatic control of theprocess, e.g., via the use of a controller, while the method of FIG. 14Brelates to manual control of the process, e.g., by a human operator.

As noted above, in various embodiments, some of the method elementsshown may be performed concurrently, in a different order than shown, ormay be omitted. Additional method elements may be performed as desired.Note that where method elements have been described earlier, thedescriptions below may be abbreviated.

As FIGS. 14A and 14B show, method elements 1202-1204, described indetail above with reference to FIG. 12, involve providing input data,e.g., measurement and/or synthesized data related to process conditionsand product properties, to a neural network where the input data has aspatial dependence or distribution (1202), the neural network generatingoutput data based on the input data, where the output data have aspatial dependence or distribution corresponding to that of the inputdata (1204).

As FIG. 14A shows, in 1206A, the output data may be provided to acontroller for use in controlling the manufacturing process.

As FIG. 14A also shows, in 1408, controller output data may be computedin accordance with the provided output data of 1206A. For example, asthe provided output data comprise predicted values for productproperties, the values may be compared to desired or target productproperties, where the differences between the predicted values and thedesired or target values may be considered errors, as is well known inthe art of predictive process control. Corresponding adjustments may bedetermined that are intended to correct for these errors. In otherwords, controller output data may be computed that attempts to modifythe process conditions of the process in such a way as to improve theresulting product properties, i.e., to move the properties toward thetarget values. Note that in embodiments where an automated controller isused, these computations may be performed automatically, e.g., bysupervisory controller 1108 via such technologies as neural networks,support vector machines, expert systems, fuzzy logic, etc., as desired.

As FIG. 14A further indicates, in 1410, the process may be controlledbased on the controller output data. For example, the controller outputdata may be used to control an actuator array to change a controllableprocess condition or state of the process in accordance with thecontroller output data. In other words, the actuator array may include aplurality of actuators, where the actuators have a spatial distributioncorresponding to the spatial distribution of the input data. It shouldbe noted that data that have corresponding spatial distributions do notnecessarily have the same resolution. In other words, it may be the casethat output data with a number of data sets, e.g., 256, may have aspatial distribution that corresponds to that of input data with a muchlarger number of data sets, e.g., 1024, in which case there are fourinput data for each output datum. Thus, the spatial distribution ofdifferent data may correspond, but not be at the same resolution orspatial frequency.

Alternatively, as indicated in FIG. 14B, in 1206B, the output data maybe provided to an operator, e.g., a human operator for use incontrolling the manufacturing process.

As FIG. 14B also shows, in 1411, the operator may manually control theprocess based on the output data of 1206B. In other words, inembodiments that rely on manual operator control, a human operator mayuse the output data from the neural network to manually and directlycontrol the process. For example, in one embodiment, the operator maymanually control an actuator array to change a controllable processstate in accordance with the output data. The actuator array may includea plurality of actuators, where the plurality of actuators have spatialrelationships corresponding to the spatial relationships of the inputdata.

As FIGS. 14A and 14B indicate, in 1412, process conditions and productproperties may be measured, e.g., via sensors, such as traversingsensors and/or static sensor arrays, resulting in process condition andproduct property measurement data, as described above with reference toFIGS. 11A and 11B.

In 1414, additional data may optionally be synthesized to augment and/orreplace at least a portion of the measurement data, as also describedabove with reference to FIGS. 11A and 11B.

Finally, as also shown in FIGS. 14A and 14B, the measured (andoptionally synthesized) process condition and product property data maybe provided as new input to the neural network, and the method mayrepeat as desired, controlling the process in an iterative manner toproduce a product with the desired properties.

Thus, in embodiments of the methods of FIGS. 14A and 14B, because of thecorresponding spatial distributions (e.g., common spatial coherence) ofthe input data, the output data, (the controller output data of FIG.14A,) and the actuators, the process control may be implemented andperformed taking into account the spatial nature of the product andcontrolling such properties accordingly. Thus, for example, if theproduct were found or predicted to have, say, a strip with sub-standardproperty along one side of the web or sheet, the controller output datamay indicate that a corresponding actuator should be adjusted in such away as to correct this problem. For example, the product's propertiesmay be managed at the CD zone level. Thus, various embodiments of thepresent invention may facilitate increased subtlety and resolution inthe control of web-based products.

Note that in some embodiments, the neural network may be occasionally betrained, e.g., may periodically be updated, e.g., using data that havebeen accumulated regarding the operation and performance of the process.For example, each cycle of the process may include storing measured dataregarding the process conditions and product properties in historicaldatabases, as described above. These data may be used to provide furthertraining of the neural network. In some embodiments, various analysesmay be performed on the data, where the results of such analyses mayalso be used to train the neural network.

In various embodiments, the neural network may be trained offline oronline. For example, in offline embodiments, the neural network may beupdated or retrained without being in direct control of or connected tothe operating manufacturing process, or, in preferred embodiments, aclone of the neural network may be trained and effectively swapped outwith the production neural network, e.g., two or more copies of datadefining the neural network's configuration may be maintained, where anoffline version is trained using the historical (or other) data, andwhere the production neural network may be reconfigured to reflect thetraining, possibly without stopping the process at all. In oneembodiment, a developer may configure the neural network using atemplate approach. For example, a graphical user interface may beprovided whereby the developer may interactively specify the neuralnetwork architecture, as well as other details of the neural network'soperation.

Alternatively, or in addition to, the neural network may be trainedonline, e.g., may be trained while the process is in operation, e.g.,via incremental training, where the neural network is modified by smallamounts over the course of operation of the process.

Various embodiments of the system and method described herein maysubstantially ameliorate problems related to process dead time,measurement dead time, data time frequency, data spatial frequency, andmeasurement variability in both process condition measurement array dataand product property measurement array data, and so may improve controlin both the time domain, i.e., in the manufacturing direction, and inthe spatial domain, i.e., the cross manufacturing direction.

Various embodiments further include receiving or storing instructionsand/or data implemented in accordance with the foregoing descriptionupon a computer-readable medium. Suitable media include a memory mediumas described above, as well as any other medium accessible by acomputer, and operable to store computer-executable programinstructions.

Although the system and method of the present invention has beendescribed in connection with the preferred embodiment, it is notintended to be limited to the specific form set forth herein, but on thecontrary, it is intended to cover such alternatives, modifications, andequivalents, as can be reasonably included within the spirit and scopeof the invention as defined by the appended claims.

1. A method for controlling a process with spatially dependentconditions for producing a product with spatially dependent properties,comprising: synthesizing, via a machine learning method, one or moreprocess conditions at each of one or more spatial positions in aproduction line of a process or synthesizing one or more productproperties at each of one or more spatial positions on the product togenerate input data, wherein synthesizing comprises generatingadditional input data using nonlinear models based on historical spatialrelationship data; providing the input data to a process control system,wherein the input data comprise a plurality of input data sets, eachinput data set comprising values for a set of one or more inputparameters; and generating output data in accordance with the inputdata, wherein the output data comprise a plurality of output data sets,each output data set comprising values for a set of one or more outputparameters, each output parameter comprising a predicted processcondition or product property, wherein the output data preserve spatialrelationships of the output data corresponding to the spatialrelationships of the input data, and wherein the output data are useableby the process control system or an operator to control the process. 2.The method of claim 1, wherein providing the input data comprises:measuring one or more process conditions at each of one or more spatialpositions in a production line of the process to generate the inputdata; or measuring one or more product properties at each of one or morespatial positions on the product to generate the input data.
 3. Themethod of claim 1, comprising training the process control system usingsynthesized process condition data having spatial relationshipscorresponding to the spatial relationships of the input data.
 4. Themethod of claim 1, comprising pre-processing the input data prior toproviding the input data to the process control system.
 5. The method ofclaim 4, wherein pre-processing the input data comprises one or more of:removing and/or replacing unusable or invalid data with valid data; orputting the data into a more usable form or format.
 6. The method ofclaim 1, wherein the process control system comprises a plurality ofsub-networks.
 7. The method of claim 6, wherein each of the plurality ofsub-networks corresponds to a respective data set in the plurality ofdata sets and each input parameter in each data set corresponds to arespective input node of a sub-network.
 8. The method of claim 1,wherein synthesizing comprises one or more of: interpolating measureddata; or extrapolating measured data.
 9. The method of claim 1,comprising: providing the output data to an operator; and controlling anactuator array via manual input by the operator to change a controllableprocess state in accordance with the output data, wherein the actuatorarray comprises a plurality of actuators, and wherein the plurality ofactuators have spatial relationships corresponding to the spatialrelationships of the input data.
 10. The method of claim 1, comprising:providing the output data to a controller; controlling an actuator arrayvia the controller to change a controllable process state in accordancewith the controller output data, wherein the actuator array comprises aplurality of actuators, and wherein the plurality of actuators havespatial relationships corresponding to the spatial relationships of theinput data.
 11. The method of claim 10, comprising performing the stepsof the method in an iterative manner to control production of theproduct with desired properties.
 12. The method of claim 11, comprisingperiodically updating the process control system based on measuredprocess conditions and product properties.
 13. The method of claim 1,wherein each input data set comprises position information for the setof input parameters, and wherein the position information indicates thespatial relationships of the input data; and wherein each output dataset comprises position information for the one or more outputparameters.
 14. The method of claim 1, wherein the input data areprovided to inputs of the process control system in a manner thatpreserves the spatial relationships of the input data; wherein theoutput data are provided by outputs of the process control system in amanner that preserves spatial relationships of the output data; andwherein the spatial relationships of the output data correspond to thespatial relationships of the input data.
 15. A method for controlling aweb-based manufacturing process with spatially dependent conditions forproducing a web product with spatially dependent properties, comprising:synthesizing, via a machine learning method, one or more processconditions at each of one or more spatial positions in a production lineof a web-based manufacturing process or synthesizing one or more webproduct properties at each of one or more spatial positions on the webproduct to generate input data, wherein synthesizing comprisesgenerating additional input data using nonlinear models based onhistorical spatial relationship data; providing the input data to aprocess control system, wherein the input data comprise a plurality ofinput data sets, each input data set comprising values for a set of oneor more input parameters; and generating output data in accordance withthe input data, wherein the output data comprise a plurality of outputdata sets, each output data set comprising values for a set of one ormore output parameters, each output parameter comprising a predictedprocess condition or web product property, wherein the output datapreserve spatial relationships of the output data corresponding to thespatial relationships of the input data, and wherein the output data areuseable by the process control system or an operator to control theweb-based manufacturing process.
 16. The method of claim 15, whereinproviding the input data comprises: measuring one or more processconditions at each of one or more spatial positions in a production lineof the web-based manufacturing process to generate the input data; ormeasuring one or more web product properties at each of one or morespatial positions on the web product to generate the input data.
 17. Themethod of claim 15, comprising training the process control system usingsynthesized process condition data having spatial relationshipscorresponding to the spatial relationships of the input data.
 18. Themethod of claim 15, comprising pre-processing the input data prior toproviding the input data to the process control system.
 19. The methodof claim 18, wherein pre-processing the input data comprises one or moreof: removing and/or replacing unusable or invalid data with valid data;or putting the data into a more usable form or format.
 20. A system forcontrolling a process with spatially dependent conditions for producinga product with spatially dependent properties, comprising: a processor;and a memory coupled to the processor that stores program instructionsexecutable by the processor to implement a process control system,wherein the process control system comprises: a plurality of inputs,operable to receive input data, wherein the input data comprise aplurality of input data sets, each comprising values for a set of one ormore input parameters, and wherein the input data are provided by one ormore of: synthesizing, via a machine learning method, one or moreprocess conditions at each of one or more spatial positions in aproduction line of the process to generate the input data, orsynthesizing one or more product properties at each of one or morespatial positions on the product to generate the input data, whereinsynthesizing comprises generating additional input data using nonlinearmodels based on historical spatial relationship data; wherein theprocess control system is operable to generate output data in accordancewith the input data, wherein the output data comprise a plurality ofoutput data sets, each output data set comprising values for a set ofone or more output parameters, each output parameter comprising apredicted process condition or product property, wherein the output datapreserve spatial relationships of the output data corresponding to thespatial relationships of the input data.